Toxicity prediction using target, interactome, and pathway profiles as descriptors

Füzi, Barbara; Mathai, Neann; Kirchmair, Johannes; Ecker, Gerhard

doi:10.1016/j.toxlet.2023.04.005

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Several different fields of computational chemistry have already experienced the benefits given by artificial intelligence, with a special focus on the early discovery environment [138,139]. One very relevant example is represented by on-target and off-target effect predictions in computational toxicology [140][141][142], which is configured in the family of AI-based methods for target prediction based only on ligand chemical data. On the structure-based side, deltalearning [143], deep learning-based 3D pocket mapping [144], and AI rescoring techniques have been developed and documented in the recent years.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Bassani

Moro

2023

Molecules

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The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of “computer-aided drug design” (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Bassani

Moro

2023

Molecules

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“…In the context of read-across analyses, binary chemical fingerprints are extensively used for constructing predictive models for diverse biological activities. ,, To be successful, a read-across approach based on structural similarity requires more than just comparing pairs of compounds; it involves grouping multiple compounds with similar chemical structures. ,, However, analyzing data sets like Tox21, which contain a large number of compounds ( n = 8435), presents challenges due to the high-dimensional nature of chemical fingerprints. This complexity complicates the direct association of these fingerprints with HTS results (Figure A).…”

Section: Resultsmentioning

confidence: 99%

“…From a regulatory perspective, for read-across to be acceptable, several factors must be considered, including structural similarity, common metabolites, similar toxicokinetic profiles, comparable physicochemical properties, and shared biological targets and metabolic pathways. − Although structural similarity alone may not be sufficient to justify read-across predictions, the Read-Across Assessment Framework guideline issued by the European Chemicals Agency requires that any read-across approach must be based on structural similarity between the source and target substances as a prerequisite . Thus, diverse chemical fingerprint-based similarity comparisons have been commonly used in in silico read-across to predict biological activity or toxicity of chemicals. ,,,, …”

Section: Introductionmentioning

confidence: 99%

Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist

An,

Park,

Hwang

et al. 2024

Chem. Res. Toxicol.

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This study introduces a novel cheminformatic readacross approach designed to identify potential environmental obesogens, substances capable of disrupting metabolism and inducing obesity by mainly influencing nuclear hormone receptors (NRs). Leveraging real-valued two-dimensional features derived from chemical fingerprints of 8435 Tox21 compounds, cluster analysis and subsequent statistical testing revealed 385 clusters enriched with compounds associated with specific NR targets. Notably, one cluster exhibited selective enrichment in peroxisome proliferator-activated receptor γ (PPARγ) agonist activity, prominently featuring methoxy cinnamate ultraviolet (UV) filters and obesogen-related compounds. Experimental validation confirmed that 2-ethoxyethyl 4-methoxycinnamate, an organic UV filter cinoxate, could selectively bind to PPARγ (K i = 18.0 μM), eliciting an obesogenic phenotype in human bone marrow-derived mesenchymal stem cells during adipogenic differentiation. Molecular docking and further experiments identified cinoxate as a potent PPARγ full agonist, demonstrating a preference for coactivator SRC3 recruitment. Moreover, cinoxate upregulated transcription levels of genes encoding lipid metabolic enzymes in normal human epidermal keratinocytes as primary cells exposed during clinical usage. This study provides compelling evidence for the efficacy of cheminformatic read-across analysis in prioritizing potential obesogens, showcasing its utility in unveiling cinoxate as an obesogenic PPARγ agonist.

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“…This variability is not merely a footnote but a central consideration in the development of predictive models. Füzi et al’s 54 introduction of a systems biology approach, utilizing target, interactome, and pathway profiles, offers a fresh lens through which to view hepatotoxicity mechanisms. This approach is not just another method but also a potential revolution in understanding the biological intricacies of DILI.…”

Section: Advances In the Prediction Of Toxicity End Pointsmentioning

confidence: 99%

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

Amorim,

Piochi,

Gaspar

et al. 2024

Chem. Res. Toxicol.

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The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein−ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

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Toxicity prediction using target, interactome, and pathway profiles as descriptors

Cited by 10 publications

References 38 publications

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

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